from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
import numpy as np

class DataBalancer:
    def __init__(self, strategy='smote'):
        self.strategy = strategy
        
    def balance(self, X, y):
        """执行平衡处理"""
        if self.strategy == 'smote':
            return SMOTE().fit_resample(X, y)
        elif self.strategy == 'gan':
            return self._gan_augmentation(X, y)
        elif self.strategy == 'undersample':
            return RandomUnderSampler().fit_resample(X, y)
        else:
            raise ValueError("Unsupported balancing strategy")

    def _gan_augmentation(self, X, y):
        """使用GAN生成合成样本"""
        # 简化的GAN实现示例
        from tensorflow.keras.layers import Dense, Input
        from tensorflow.keras.models import Sequential
        
        # 创建简单的生成器
        generator = Sequential([
            Dense(64, input_dim=100, activation='relu'),
            Dense(X.shape[1], activation='tanh')
        ])
        
        # 生成合成样本（实际应用需要完整GAN实现）
        noise = np.random.normal(0, 1, (len(X), 100))
        synthetic = generator.predict(noise)
        
        return np.vstack([X, synthetic]), np.concatenate([y, np.ones(len(synthetic))]) 